SpectraSensML软件：掌握用于发光测温2.0的完整光谱信息
SpectraSensML Software: Mastering Complete Spectral Information for Luminescence Thermometry 2.0
中文总结 AI 辅助
研究发光测温传统方法局限，提出基于SpectraSensML平台的发光测温2.0范式，用机器学习回归处理全光谱。在掺Yb3+荧光粉上测试19种回归算法，多组分回归器降不确定性，开源应用可实现社区基准测试。
中文摘要 AI 辅助
发光测温经过数十年研究，专注于优化材料及从孤立光谱特征提取温度信息，如发光强度比、带宽、线移和激发态寿命。但传统方法仅利用预选光谱特征子集，丢弃了全光谱中大量温度相关信息。本文提出发光测温2.0（LT 2.0）范式，通过新开发的SpectraSensML平台实现，机器学习回归对整个发射光谱进行操作以提供温度读数。在125至700K的近红外生物透明窗口中发射的掺Yb3+荧光粉上进行了演示。对来自四个家族的19种回归算法进行了系统基准测试。结合前三个主成分的传感器融合估计器的均方根误差为0.36K，比最佳发光强度比变体提高了七倍。单组分方法在定量上次优：利用前三个主成分的多组分回归器将温度不确定性降低了近一个数量级。解释了决策树集成在未见过的温度上失败的结构原因：它们的分段常数预测无法在训练设定点之外进行插值。与手稿一起发布了用于获得结果的开源SpectraSensML应用程序，以实现可重复的社区基准测试。
英文摘要
Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here, Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb3+-doped phosphor emitting in the near-infrared biological transparency window across 125 to 700 K. Nineteen regression algorithms drawn from four families, namely tree ensembles, physics-aware regression models, kernel and instance methods, and neural networks, are systematically benchmarked. A sensor-fusion estimator that combines the first three principal components reaches an root-mean-square error of 0.36 K, a seven-fold improvement over the best luminescence intensity ratio variant. Single-component approaches are shown to be quantitatively sub-optimal: multi-component regressors that exploit the first three principal components reduce the temperature uncertainty by close to an order of magnitude. The structural reason behind the failure of decision-tree ensembles on unseen temperatures is explained: their piecewise-constant predictions cannot interpolate beyond training set-points. The open-source SpectraSensML application used to obtain the results is released alongside the manuscript to enable reproducible community benchmarks.
